Results 81 to 90 of about 5,001,817 (246)

MTMEGPS: An R package for multi-trait and multi-environment genomic and phenomic selection using deep learning

Frontiers in Plant Science
Genomic and phenomic selection have transformed modern breeding by enabling data-driven prediction of complex traits. Deep learning (DL) can further enhance predictive ability by capturing nonlinear patterns that classical and Bayesian approaches often ...
Freddy Mora-Poblete, Javiera Valenzuela-Herrera, Matías Balach, Claudio J. Martínez-Araya, Carlos Maldonado   +4 more
doaj   +1 more source

Hyperparameter Optimization for Effort Estimation

CoRR, 2018
Software analytics has been widely used in software engineering for many tasks such as generating effort estimates for software projects. One of the "black arts" of software analytics is tuning the parameters controlling a data mining algorithm. Such hyperparameter optimization has been widely studied in other software analytics domains (e.g.
Tianpei Xia, Rahul Krishna, Jianfeng Chen, George Mathew, Xipeng Shen, Tim Menzies   +5 more
openaire   +3 more sources

Reconfigurable Au Nanoparticle Monolayers on Regenerated Cellulose Hydrogels: Highly Sensitive SERS Detection of Polystyrene Micro/Nanoplastics With Interpretable Deep Learning

Advanced Functional Materials, EarlyView.
A regenerated cellulose (RC) hydrogel‐based SERS substrate integrating a Marangoni‐transferred gold nanoparticle self‐assembled monolayer (Au‐SAM) is fabricated. Reswelling‐induced hotspot formation enhances polystyrene micro/nanoplastics (PS MNPs) detection in complex matrices, providing reproducible, high‐throughput SERS signals across diverse ...
Youngho Jeon, Yu‐Jin Jeon, Suji Lee, Jeseong Kim, Dae‐Hyun Jung, Jungmok You   +5 more
wiley   +1 more source

Adaptive Optimizer for Automated Hyperparameter Optimization Problem

CoRR, 2022
The choices of hyperparameters have critical effects on the performance of machine learning models. In this paper, we present a general framework that is able to construct an adaptive optimizer, which automatically adjust the appropriate algorithm and parameters in the process of optimization.
openaire   +2 more sources

Artificial Intelligence‐Assisted Workflow for Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling

Advanced Materials, EarlyView.
AI‐Assisted Workflow for (Scanning) Transmission Electron Microscopy: From Data Analysis Automation to Materials Knowledge Unveiling. Abstract (Scanning) transmission electron microscopy ((S)TEM) has significantly advanced materials science but faces challenges in correlating precise atomic structure information with the functional properties of ...
Marc Botifoll, Ivan Pinto‐Huguet, Enzo Rotunno, Thomas Galvani, Catalina Coll, Payam Habibzadeh Kavkani, Maria Chiara Spadaro, Yann‐Michel Niquet, Martin Børstad Eriksen, Sara Martí‐Sánchez, Georgios Katsaros, Giordano Scappucci, Peter Krogstrup, Giovanni Isella, Andreu Cabot, Gonzalo Merino, Pablo Ordejón, Stephan Roche, Vincenzo Grillo, Jordi Arbiol   +19 more
wiley   +1 more source

Brain tumor classification from MRI scans: a framework of hybrid deep learning model with Bayesian optimization and quantum theory-based marine predator algorithm

Frontiers in Oncology
Brain tumor classification is one of the most difficult tasks for clinical diagnosis and treatment in medical image analysis. Any errors that occur throughout the brain tumor diagnosis process may result in a shorter human life span.
Muhammad Sami Ullah, Muhammad Attique Khan, Anum Masood, Olfa Mzoughi, Oumaima Saidani, Nazik Alturki   +5 more
doaj   +1 more source

LEAD: Literature Enhanced Ab Initio Discovery of Nitride Dusting Layers for Enhanced Tunnel Magnetoresistance and Lower Resistance Magnetic Tunnel Junctions

Advanced Materials, EarlyView.
Magnetic tunnel junctions (MTJs) using MgO tunnel barriers face challenges of high resistance‐area product and low tunnel magnetoresistance (TMR). To discover alternative materials, Literature Enhanced Ab initio Discovery (LEAD) is developed. The LEAD‐predicted materials are theoretically evaluated, showing that MTJs with dusting of ScN or TiN on ...
Sabiq Islam, William S. Rogers, Chen‐Yu Hu, Ming‐Yuan Song, Xinyu Bao, Shehrin Sayed, Jean Anne C. Incorvia   +6 more
wiley   +1 more source

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

Effect of hyperparameter tuning of machine learning algorithms on the modeling quality of the distribution of three mosquito species in Morocco

Journal of Intelligent Systems
The widespread use of machine learning algorithms in dataset modeling requires a thorough understanding of the various tools likely to improve the modeling quality.
Douider Meriem, Amrani Ibrahim, Abik Mounia   +2 more
doaj   +1 more source

Data‐Driven Materials Science for Energy‐Sustainable Applications

Advanced Materials, EarlyView.
Data‐driven approaches powered by artificial intelligence are transforming materials discovery for energy sustainability. This review examines how auto‐generated high‐quality materials databases and domain‐specific language models accelerate research in photovoltaics, thermoelectrics, batteries and magnetic materials. Applications involve extraction of
Jacqueline M. Cole
wiley   +1 more source